Tumor and peritumor radiomics analysis based on contrast-enhanced CT for predicting early and late recurrence of hepatocellular carcinoma after liver resection

Abstract

Background In China, liver resection has been proven to be one of the most important strategies for hepatocellular carcinoma patients, but the recurrence rate is high. This study sought to investigate the prognostic value of pretreatment tumor and peritumor contrast-enhanced CT radiomics features for early and late recurrence of BCLC stage 0-B hepatocellular carcinoma after liver resection. Methods This study involved 329 hepatocellular carcinoma patients after liver resection. A radiomics model was built by using Lasso-Cox regression model. Association between radiomics model and recurrence-free survival was explored by using Harrell’s concordance index (C-Index) and receiver operating characteristic (ROC) curves. Then, we combined the radiomics model and clinical factors to establish a nomogram whose calibration and discriminatory ability were revealed. Results Ten significant tumor and peritumor features were screened to build the radiomics model whose C-indices were 0.743 [95% CI, 0.707 to 0.778] and 0.69 [95% CI, 0.629 to 0.751] in the training and validation cohorts. Moreover, the discriminative accuracy of the radiomics model improved with peritumor features entry. The C-indices of the combined model were 0.773 [95% CI, 0.739 to 0.806] and 0.727 [95% CI, 0.667 to 0.787] in the training and validation cohorts, outperforming the radiomics model. Conclusions The tumor and peritumor contrast-enhanced CT radiomic signature is a quantitative imaging biomarker that could improve the prediction of early and late recurrence after liver resection for hepatocellular carcinoma patients when used in addition to clinical predictors.

Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma

“…Previous studies have reported that radiomics analysis of intratumoral combined 3 mm, 5 mm, and 10 mm peritumoral regions can provide valuable information for prognosis prediction in HCC [ 18 , 19 , 30 , 39 , 40 ]. In addition, according to the practice guidelines for the pathological diagnosis of primary liver cancer (2015 update) [ 41 ], the liver tissues within 10 mm surrounding the tumor are defined as the adjacent areas around the cancer, where the probability of MVI is high.…”

Effect of multiple peritumoral regions of interest ranges based on computed tomography radiomics for the prediction of early recurrence of hepatocellular carcinoma after resection

“…In terms of radiomics, Previous studies have found that peritumoral radiomics is more valuable in assessing tumor heterogeneity ( 28 , 29 ). However, existing radiomic studies are controversial with regard to the range of peritumoral ROIs for predicting HCC recurrence ( 20 , 30 , 31 ). Therefore, we constructed multiple models with different ranges of ROI radiomics to explore the highly aggressive peritumoral region.…”

Computed tomography radiomics signature via machine learning predicts RRM2 and overall survival in hepatocellular carcinoma

“…Furthermore, the combination of AFP and radiomic characteristics can effectively predict GPC3 expression (respective AUC values for the training and validation groups: 0.926 and 0.914). In this research, four features were screened out from 107 imaging features to construct a whole-tumor model, and a whole-tumor and peritumoral model was constructed using the same method considering micrometastasis and microinfiltration of immune-associated cells of the surrounding area of HCC, which was used less frequently in previous studies ( 21 , 22 ). Two logistic regression models were built to predict the level of RRM2 expression.…”